Probability & Statistics · Mathematics & Statistics
Statistics Theory
The newest papers in Statistics Theory that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
321 papers, from 31 August 2026 to 4 November 2026: 287 arXiv preprints and 34 OpenAlex records.
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Newest papers, page 12
A statistical theory for blind denoising: minimax estimation of the noise level from a single sample
Motivated by blind denoising in diffusion models, we study estimation of an unknown Gaussian noise level from a single high-dimensional observation, assuming the signal law P is known.On the asymptotic shape of quantile surfaces
This article is concerned with the asymptotic shape of quantile surfaces, defined as the set of quantiles at a given level $α$ generated by a controlled one-dimensional distribution.Nonparametric In-Context Learning under Growing Geometric Complexity: Minimax Optimality and Local Geometry-Adaptivity of Transformers
Transformers have become a central architecture for in-context learning (ICL), particularly through their state-of-the-art performance in large language models.Statistical attribute alignment for black-box generative AI via output post-processing
Generative AI systems are increasingly used, but aligning their outputs with user requirements poses a continuing challenge.Classical Sufficiency in Quantum Statistical Experiments
A quantum measurement transforms a quantum statistical experiment into a classical one, but different measurements generally yield experiments with different statistical information.Riemannian Gradient Descent for Gaussian Mixture Models with unknown diagonal covariances
This paper investigates the numerical resolution of the Beurling-LASSO (BLASSO), a convex optimization framework that promotes sparsity in the space of measures.Scaffold-Constrained Subset Dynamic Programming for Exact SSE Clustering
Exact Euclidean \(K\)-means partitions \(n\) observations into \(K\) unlabelled clusters, but the unrestricted search is generally exponential.High-dimensional Gaussian Graphical Model Testing for Long-Memory Time Series
Many real-world high-dimensional time series exhibit long-memory, but Gaussian graphical model testing in this regime remains understudied.Change detection with conformal martingales: new optimal constructions, and suboptimality of existing methods
We study distribution-free sequential changepoint detection for independent observations with unknown and unrestricted pre- and post-change laws.On the Sample Complexity of Active Learning with Membership Queries
This work revisits a fundamental question in active learning: how powerful is the ability to synthesize arbitrary queries?Robustness of Diffusion Models under Distribution Shift
Score-based diffusion models are increasingly considered in settings where the underlying data distribution may differ from the training distribution, yet existing theoretical guarantees largely…Even Sharper Bounds for Transductive Learning and Its Applications
We introduce Sharper Transductive Local Complexity (STLC), a localized complexity method for transductive learning under uniform sampling without replacement.Exact Bayes Regret and Asymptotic Optimality in High-Dimensional Gaussian Bandits
We study Bayesian linear bandits with an isotropic Gaussian parameter, independent Gaussian candidate arms, and Gaussian reward noise when the horizon is proportional to the dimension.Eigenvalue and Eigenvector Approximation for Random Matrices Using Low-Degree Polynomials
We initiate the study of approximating the top eigenvalue and eigenvector of a random symmetric matrix $ A \in \mathbb{R}^{n\times n} $ using $ q(A)b $ where $q$ is a degree-$d$ polynomial and $b$…Adaptive Random Matrices in Gaussian Bandits: Spectral Universality and Selection-Induced Outliers
Adaptive arm selection changes the distribution of the observations collected by a bandit algorithm, but it need not change their limiting empirical spectrum.Statistical Gains from Looped Estimation under Parameter Budgets
Memory constraints in artificial intelligence motivate accurate function approximation with fewer parameters.Statistical Rates for Entropic Optimal Transport in the Discrete to SubGaussian Regime
We study statistical rates in entropic optimal transport in the semi-discrete regime where one measure has finite support and the other is subGaussian.Context-Adaptive Thresholding for Conditionally Representative Monitoring and Classification
Commonly, classifiers and monitoring procedures are trained from labeled data by optimizing an objective such as the misclassification rate.Rolling Conformal Prediction in Sequential Model Training
We introduce Rolling Conformal Prediction (rolling-CP), a distribution-free predictive inference method for the setting of sequential model training.Dual Boundary Condition Inference: A Two-Boundary Product Rule and Its Implications
Many inference problems are constrained from two sides: forward information and a second constraint on outcomes.